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News

  • Pytorch official code of QANet is released. (03/11/2021)

Introduction

Official implementation of Quality-Aware Network for Human Parsing

In this repository, we release the QANet code in Pytorch.

  • QANet architecture:

Citing QANet

If you use QANet, please use the following BibTeX entry.

@inproceedings{yang2021qanet,
  title = {Quality-Aware Network for Human Parsing},
  author = {Lu Yang and Qing Song and Zhihui Wang and Zhiwei Liu and Songcen Xu and Zhihao Li},
  booktitle = {arXiv preprint arXiv:2103.05997},
  year = {2021}
}

Installation

  • 8 x TITAN RTX GPU
  • pytorch1.6
  • python3.6.8

Install QANet following INSTALL.md.

Data Prepare

Please follow DATA_PREPARE.md to download training and evaluating data.

Results and Models

QANet On CIHP

Backbone mIoU APp/APp50/PCP50 APr/APr50 DOWNLOAD
ResNet50 62.9 60.1/74.3/68.9 56.2/63.5 GoogleDrive
ResNet101 64.1 62.0/77.9/72.4 57.9/65.6
HRNet-W48 66.1 64.5/81.3/75.7 60.8/68.8 GoogleDrive

QANet On LIP

Backbone Input Size pixAcc. meanAcc. mIoU DOWNLOAD
HRNet-W48 512×384 88.92 71.87 59.61 GoogleDrive
HRNet-W48 544×416 89.19 72.97 60.52 GoogleDrive
  • Flip test is used.
  • For CIHP, we use FCOS-R50 to detect person (73.1 AP on CIHP val).
  • Multi-scale test is used for LIP.

ImageNet pretrained weights

please put the pretrained weights in QANet/weights

Training

To train a model with 8 GPUs run:

python tools/train_net_all.py --cfg cfgs/CIHP/QANet/QANet_R-50c_512x384_1x.yaml

Evaluation

multi-gpu evaluation,

python tools/test_net_all.py --cfg ckpts/CIHP/QANet/QANet_R-50c_512x384_1x/QANet_R-50c_512x384_1x.yaml --gpu_id 0,1,2,3,4,5,6,7

single-gpu evaluation,

python tools/test_net_all.py --cfg ckpts/CIHP/QANet/QANet_R-50c_512x384_1x/QANet_R-50c_512x384_1x.yaml --gpu_id 0

License

QANet is released under the MIT license.